Integrated aircraft and passenger recovery with cruise time controllability

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Arikan U., GÜREL S., Akturk M. S.

ANNALS OF OPERATIONS RESEARCH, vol.236, no.2, pp.295-317, 2016 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 236 Issue: 2
  • Publication Date: 2016
  • Doi Number: 10.1007/s10479-013-1424-2
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.295-317
  • Keywords: Airline operations, Disruption management, Irregular operations, Passenger recovery, Cruise speed control, Conic quadratic mixed integer programming, DISRUPTION MANAGEMENT, AIRLINE, OPERATIONS, MODELS


Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.